Cyberinfrastructure for Network Science Center at SLIS, Indiana University is pleased to announce the release of Network Workbench 1.0.0 beta. This release is a development release, neither fully tested nor bug-free. We believe that the tool contains such significant new functionalities that it would be in our users' interest to make it available now In the coming month we plan to make the Network Workbench 1.0.0 stable release, which will contain fixes for various known bugs. Additional functionalities and improvements will also be introduced in the coming months, based upon the user feedback we receive.

2. General Description

The Network Workbench tool is a network analysis, modeling, and visualization toolkit for biomedical, social science, and physics research. It is a standalone desktop application requiring Java 1.4+ JRE. The tool installs and runs on Windows, Mac, and Linux platforms.

Network Workbench uses the Cyberinfrastructure Shell (CIShell) to bring together various algorithms used in the Network Science community. CIShell enables data and algorithms from disparate sources to work together, integrating with Java-based algorithms as well as algorithms developed in other programming languages such as FORTRAN, C, and C++.

CiShell, and by extension Network Workbench, is based on OSGi, a Java framework for plugin-based service-oriented architectures.

The full list of algorithms available in Network Workbench can be found on the NWB Community Wiki.

If you would like to learn more about the Network Workbench project and the tool in general, please visit our main website at http://nwb.slis.indiana.edu.

To uninstall the NWB tool, run Uninstall NWB from your operating system program menu or run uninstaller.jar (in {NWB Installation Directory}/Uninstaller).

In order to utilize NWB's plotting functionality, Mac and Linux users should install Gnuplot.

In order to use the LaNet visualization algorithm, users of all platforms should install POV-Ray.

4. What’s New

4.1 Expanded Scientometrics Functionality
One of the main focuses of this release is our expanded scientometrics functionality, which allows users to process, analyze, and visualize science publication data from a variety of sources, including ISI, Scopus, Endnote, Bibtex, and NSF. We currently support the extraction of paper citation, co-author, co-citation, co-word, and bibliographic coupling networks from scientometrics data sources, as well as providing more advanced open-ended functionality, which is flexible enough to create a wide variety of other networks. This release also contains support for basic cleaning and merging of nodes in networks, which can be helpful when dealing with less than perfectly clean data. Our scientometrics tutorial describing how to use the new functionality is part of the NWB manual.

4.2 New Preprocessing Algorithms
The NWB 1.0.0 beta release includes a variety of new preprocessing algorithms, including various network trimming algorithms, the Normalize Text algorithm, and the Slice Table by Time algorithm.

The new network trimming algorithms-- Extract Top Nodes, Extract Top Edges, Extract Node Above or Below Value, and Extract Edges Above or Below Value-- allow the removal of nodes and edges based on the values of their attributes, making it easy to filter out nodes and edges which are not relevant to your analysis.

Normalize Text removes stop words and stems remaining words from text table columns, making it easier to analyze text with algorithms such as Burst Detection or when extracting co-word networks.

Slice Table by Time allows the user to break a table into one or more tables, where each table contains data for a 'slice' of time (day, year, decade, etc.). This makes time-series analysis easier and will help support future time-series algorithms.

4.3 Dynamic Network Analysis Toolkit
The Dynamic Network Analysis Toolkit allows users to create and analyze discrete dynamic network models. A discrete network model, in this context, refers to a discrete, deterministic model described by a finite number of states along with functions to describe all state transitions. These models have been applied successfully to a range of biological systems, but can also be used to study the dynamics of non-biological systems as well. A tutorial for the Dynamic Network Analysis Toolkit can be found at http://nwb.slis.indiana.edu/Docs/NWB-DND-tutorial.pdf.

4.4 Export Capability For Various Visualizations
Three visualization algorithms (Radial Tree/Graph with Annotation, Force Directed with Annotation, and Fruchterman-Reingold with Annotation) can now export to postscript, and Gnuplot is now capable of export data plots for inclusion in PDFs. To learn how to utilize these features, check the NWB FAQ.

4.6 Bug Fixes and Other Improvements
The Network Workbench team has been hard at work fixing bugs and making many other small improvements in response to feedback from our users—and this feedback is very welcome and very important to the success of NWB. If you would like to request a feature or improvement, or just report a bug or some other issue, feel free to send us an e-mail at nwb-helpdesk@googlegroups.com, or post a bug on our bug tracker.

5. Known Issues

As we mentioned previously, this release is not fully stable, and still contains issues. Here we list some of the more noticeable problems. We hope that knowing about these problems will help you should you encounter them, and also give you a glimpse at what we intend to fix in the future.

5.1 Some Algorithms Fail Silently
In some cases layouts in GUESS or other algorithms will fail silently, without providing any obvious clue as to their cause of failure. This usually occurs when an algorithm runs out of available memory. If this happens to you, you can try increasing the memory allocated to Network Workbench by following the directions in the NWB FAQ. If that does not help, please report the bug to us at nwb-helpdesk@googlegroups.com or on our bug tracker.

5.2 Search Algorithms
The search algorithms are difficult to use in their current form, most notably because they require users to know ID numbers of nodes inside the graph. These will have to be reworked.

5.3 Visualization Algorithms
Several visualization algorithms will fail to display properly under certain circumstances, and are generally lacking in features. GUESS is currently the most flexible and powerful visualization option available to NWB users, however it can have a steep learning curve for much of its functionality. In future releases we hope to improve the usability of GUESS, as well as providing more detailed documentation on how best to use it with Network Workbench.